[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-00b436bc7770c09b-harness-beats-model-6x-agent-performance-gap-summary":3,"summaries-facets-categories":100,"summary-related-00b436bc7770c09b-harness-beats-model-6x-agent-performance-gap-summary":3686},{"id":4,"title":5,"ai":6,"body":13,"categories":54,"created_at":55,"date_modified":55,"description":47,"extension":56,"faq":55,"featured":57,"kicker_label":55,"meta":58,"navigation":81,"path":82,"published_at":83,"question":55,"scraped_at":84,"seo":85,"sitemap":86,"source_id":87,"source_name":88,"source_type":89,"source_url":90,"stem":91,"tags":92,"thumbnail_url":55,"tldr":97,"tweet":55,"unknown_tags":98,"__hash__":99},"summaries\u002Fsummaries\u002F00b436bc7770c09b-harness-beats-model-6x-agent-performance-gap-summary.md","Harness Beats Model: 6x Agent Performance Gap",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",6276,1760,19200,0.00211255,{"type":14,"value":15,"toc":46},"minimark",[16,21,25,29,32,36,39,43],[17,18,20],"h2",{"id":19},"harness-os-for-llms-driving-6x-performance","Harness: OS for LLMs, Driving 6x Performance",[22,23,24],"p",{},"A harness turns a raw LLM (the inert CPU) into an agent by managing context (RAM), databases (disk), tools (drivers), and loops for actions, observations, and iteration. It structures nine components like runtime charter (state, contracts, sub-agents) and control logic. Same model + different harness = 6x performance gap, as seen running complex prompts in Claude Code vs. Cursor: varying reasoning paths, token spend, success rates. Focus here first—model choice is secondary.",[17,26,28],{"id":27},"tsinghua-ablations-subtract-to-win-natural-language-boosts","Tsinghua Ablations: Subtract to Win, Natural Language Boosts",[22,30,31],{},"Tsinghua (Pan et al., March 2024) ablated harnesses on SWE-Bench (GPT-4o max reasoning): full harness hit 74-76% success but wasted 16.3M tokens\u002Fsample (600+ tool calls, 32+ min); stripped version used 1.2M tokens (51 calls, \u003C7 min)—14x less compute for identical results. Key: self-evolution helped consistently; verifiers hurt (-0.8 SWE-Bench, -8.4 OS-World); multi-candidate search hurt (-5.6). Migrating OSWorld desktop automation from code to structured natural language harness: success 30.4% → 47.2% (+16.8 pts), runtime 361 → 41 min, calls 1200 → 34. Natural language enables isolated testing\u002Fswaps for clean experiments.",[17,33,35],{"id":34},"stanford-auto-optimization-transferable-across-models","Stanford Auto-Optimization: Transferable Across Models",[22,37,38],{},"Omar Katab (DSPy creator, Stanford) auto-optimized harnesses via LLM (Claude 3 Opus): analyzes raw failure traces (not summaries—summaries drop accuracy 50% → 34.9%), rewrites full harness (structured retrieval, memory, topology). Scaled to 10M tokens\u002Fiteration, 400x more feedback, 82 files\u002Fround. Results: #2 TerminalBench (76.4%, auto-optimized beat hand-crafted); #1 215-text classification (+7.7 pts SOTA, 4x fewer tokens, Haiku > larger models via harness). Harness transfers: one optimized on Opus boosted five other models. Raw traces irreplaceable—details drive gains.",[17,40,42],{"id":41},"subtraction-principle-audit-prune-dont-add","Subtraction Principle + Audit: Prune, Don't Add",[22,44,45],{},"As models advance (e.g., Opus 4.6 dropped context resets), assumptions in harness components expire—prune ruthlessly (Manis rewrote 5x in 6 months; Warel cut 80% tools, improved). Builders: audit before model swaps with 4 questions: (1) Trim unnecessary context window? (2) Drop rarely used tools? (3) Remove hurting verifiers\u002Fsearch loops? (4) Rewrite control logic in natural language (+17 pts potential). Mature engineering = subtraction craft; simpler > complex.",{"title":47,"searchDepth":48,"depth":48,"links":49},"",2,[50,51,52,53],{"id":19,"depth":48,"text":20},{"id":27,"depth":48,"text":28},{"id":34,"depth":48,"text":35},{"id":41,"depth":48,"text":42},[],null,"md",false,{"content_references":59,"triage":76},[60,65,68,73],{"type":61,"title":62,"author":63,"context":64},"paper","Natural Language Agent Harness (Tsinghua)","Pan et al., Tsinghua University","cited",{"type":61,"title":66,"author":67,"context":64},"Harness Auto-Optimization (Stanford DSPy)","Omar Katab",{"type":69,"title":70,"url":71,"context":72},"tool","Data Impulse","https:\u002F\u002Fdataimpulse.com\u002F?utm_source=youtube&utm_medium=video&utm_campaign=engineerprompt","mentioned",{"type":69,"title":74,"url":75,"context":72},"Whryte","https:\u002F\u002Fwhryte.com",{"relevance":77,"novelty":78,"quality":78,"actionability":78,"composite":79,"reasoning":80},5,4,4.35,"Category: AI & LLMs. The article discusses the optimization of agent performance through harness orchestration, which is directly relevant to AI engineering and addresses the audience's need for practical applications in building AI-powered products. It provides specific insights into how different harness configurations can lead to significant performance improvements, making it actionable for developers.",true,"\u002Fsummaries\u002F00b436bc7770c09b-harness-beats-model-6x-agent-performance-gap-summary","2026-05-04 13:45:03","2026-05-04 16:11:05",{"title":5,"description":47},{"loc":82},"bcee97d1fe6f84b0","Prompt Engineering","article","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=A0xu44a1BHE","summaries\u002F00b436bc7770c09b-harness-beats-model-6x-agent-performance-gap-summary",[93,94,95,96],"agents","llm","prompt-engineering","ai-automation","Stanford\u002FTsinghua papers prove agent orchestration (harness) causes 6x performance variation on the same model; optimize harness via subtraction and natural language before switching 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Bridge this with a pipeline: Run Stockfish (top classical engine) across the full game for best-move evaluations and lines. Extract structured context via detectors for tactics (forks, pins, skewers) and positional themes (doubled pawns, structural weaknesses). Add Maya (University of Toronto neural engine) for human-like move probabilities by rating (e.g., 1500 Elo), revealing if a Stockfish-optimal move is hard to spot (low probability). Feed this JSON to an LLM solely as translator, preventing reasoning errors. Result: Nuanced commentary like \"F5 threatens to trap your queen (Bg5 line), but capture the pawn to defend and escape,\" explaining threats, defenses, and plans beyond \"bad move.\"",[22,3705,3706],{},"This grounds explanations in facts, enabling brilliant-move detection (e.g., knight sac to checkmate) with why-it-works details, plus game-phase accuracy, ratings, and opening depth insights for coaching.",[17,3708,3710],{"id":3709},"close-feedback-loops-with-autonomous-agents-for-rapid-iteration","Close Feedback Loops with Autonomous Agents for Rapid Iteration",[22,3712,3713],{},"User flags bad commentary in-app, posting position, PGN, and output to Slack while injecting into a running Claude Code session via Channels (Anthropic's research-preview MCP for event injection, like OpenClaw). Claude invokes a custom \"commentary triage skill\": Analyzes position with Stockfish\u002Fdetectors, modifies prompts or adds detectors (e.g., new tactic), regenerates commentary, self-verifies, and queries Slack (\"What specifically feels wrong?\"). Operator approves from phone, triggering PR merge. Live demo showed Claude dismissing a false flag (\"nothing wrong\") autonomously. Humans-in-loop but mobile-scale; prunes massive initial JSON context iteratively for quality gains.",[17,3715,3717],{"id":3716},"balance-sub-3s-latency-and-quality-via-model-evals-and-trade-offs","Balance Sub-3s Latency and Quality via Model Evals and Trade-offs",[22,3719,3720],{},"Consumer apps demand instant post-game reviews (cycle moves one-by-one), so target sub-3s end-to-end: Gemini 1.5 Flash delivers ~1s time-to-first-token, ~3s average total. Avoid reasoning models' unpredictable delays (no spinning \"coach thinking\" screens). Quality via 16 thematic evals (tactics, blunders, anti-hallucination) from real games: LLM-as-judge on OpenRouter for quick model swaps (Gemini Flash: 75% pass; Claude thinking: 60% but slower; GPT-4o mini: lower accuracy, moderate latency). Human experts (speakers, strong players) final-check against manual calculation. Future: Deeper chat-coach tolerates latency for reasoning models.",[22,3722,3723],{},"Key learnings: Isolate data pipelines (Stockfish\u002Fdetectors) from LLM generation for speed\u002Freliability; build clear context extractors (start big, prune); leverage domain-expert evals\u002Fpartners; agent loops enable bus-to-PR iteration.",{"title":47,"searchDepth":48,"depth":48,"links":3725},[3726,3727,3728],{"id":3699,"depth":48,"text":3700},{"id":3709,"depth":48,"text":3710},{"id":3716,"depth":48,"text":3717},[],{"content_references":3731,"triage":3748},[3732,3735,3737,3740,3743,3746],{"type":61,"title":3733,"author":3734,"context":64},"Programming a Computer to Play Chess","Claude Shannon",{"type":69,"title":3736,"context":72},"Stockfish",{"type":69,"title":3738,"author":3739,"context":72},"Maya","University of Toronto",{"type":3741,"title":3742,"context":72},"event","Deep Blue vs Kasparov",{"type":3744,"title":3745,"context":72},"other","Kaggle Game Arena LLM Chess Tournament",{"type":69,"title":3747,"context":72},"Channels (Claude Code MCP)",{"relevance":77,"novelty":78,"quality":78,"actionability":78,"composite":79,"reasoning":3749},"Category: AI & LLMs. The article provides a detailed pipeline for improving LLM performance in chess coaching, addressing the specific pain point of hallucinations in LLMs by integrating classical engines and detectors. It offers actionable insights on how to implement this system, making it relevant and practical for developers looking to enhance AI applications.","\u002Fsummaries\u002Fc87f3077967b2f97-chess-coach-pipeline-engines-detectors-llm-transla-summary","2026-05-13 15:00:06","2026-05-13 19:00:18",{"title":3689,"description":47},{"loc":3750},"c87f3077967b2f97","AI Engineer","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=FlzpEGHNVKQ","summaries\u002Fc87f3077967b2f97-chess-coach-pipeline-engines-detectors-llm-transla-summary",[94,93,95,96],"https:\u002F\u002Fi.ytimg.com\u002Fvi\u002FFlzpEGHNVKQ\u002Fhqdefault.jpg","LLMs fail at chess due to hallucinations; fix by using Stockfish for evaluation, tactical\u002Fpositional detectors for concepts, and LLM only to translate into natural language—achieving sub-3s latency without errors.","Talk by Take Take Take engineers [Anant Dole](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fanantdole\u002F) and [Asbjørn Steinskog](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fasbj%C3%B8rn-ottesen-steinskog-a8000241\u002F) on their chess app's production game-review pipeline: Stockfish evals positions, detectors flag tactics like pins\u002Fforks, LLM translates to natural explanations at sub-3s latency. They live-demo a user-feedback loop piping bad comments to Claude Code via Channels for autonomous prompt\u002Fdetector tweaks and PRs.",[96],"8lszXmxZyW7tbji7dhLGVVL6er8NS6Bp5eeHTzvg5UM",{"id":3767,"title":3768,"ai":3769,"body":3774,"categories":3811,"created_at":55,"date_modified":55,"description":47,"extension":56,"faq":55,"featured":57,"kicker_label":55,"meta":3812,"navigation":81,"path":3823,"published_at":3824,"question":55,"scraped_at":3825,"seo":3826,"sitemap":3827,"source_id":3828,"source_name":3756,"source_type":3757,"source_url":3829,"stem":3830,"tags":3831,"thumbnail_url":3832,"tldr":3833,"tweet":3834,"unknown_tags":3835,"__hash__":3836},"summaries\u002Fsummaries\u002Fb24309283167b83a-malleable-evals-adaptive-testing-for-changing-ai-a-summary.md","Malleable Evals: Adaptive Testing for Changing AI Agents",{"provider":7,"model":8,"input_tokens":3770,"output_tokens":3771,"processing_time_ms":3772,"cost_usd":3773},6788,1435,19735,0.0020526,{"type":14,"value":3775,"toc":3806},[3776,3780,3783,3786,3790,3793,3796,3800,3803],[17,3777,3779],{"id":3778},"static-benchmarks-fail-malleable-ai-systems","Static Benchmarks Fail Malleable AI Systems",[22,3781,3782],{},"Traditional software evals rely on unit tests, regression suites, CI\u002FCD, and chaos engineering to measure static code. AI agents break this: they adapt to users, rewrite harnesses like OpenClaw (where Vincent Koc is a core contributor), and exhibit behavioral drift over time. Handcrafted datasets miss 20% edge cases that break products, test suites stale quickly, and production traces reveal issues benchmarks ignore. Result: hyperfocus on static benchmarks at conferences, yet systems ship with unmeasured chaos. Trade-off: offline evals ensure compliance (e.g., no illegal financial advice) but skip real-world stretching, leaving gaps until failures hit.",[22,3784,3785],{},"Chaos engineering—randomly breaking systems to find limits—applies here but lacks in AI. Software now malleables too, shipping at lightning speed; benchmarks can't keep up without adapting.",[17,3787,3789],{"id":3788},"shift-from-prompt-to-intent-engineering-compounds-eval-challenges","Shift from Prompt to Intent Engineering Compounds Eval Challenges",[22,3791,3792],{},"AI evolved: prompt engineering (random word-bashing for outputs, like accidental painkillers from liver meds) died by 2023. Context engineering added RAG, tools, search—enabling modular testing of agent parts (e.g., sales MCP tools). Now, 2025's intent engineering: cheap, fast tokens fuel self-optimizing agents understanding user intent via harnesses like OpenClaw, Claude, or CodeEx. Models solve human-hard ARC-AGI 2\u002F3 puzzles via pattern recognition.",[22,3794,3795],{},"Problem: personalized experiences vary by user, making evals harder. Agents seem \"insecure\" without insight into layers. Need: measure ambiguity, personality rubrics (like art grading), not just 1+1=2.",[17,3797,3799],{"id":3798},"build-living-evals-as-self-optimizing-agents","Build Living Evals as Self-Optimizing Agents",[22,3801,3802],{},"Define end-state intent (e.g., user reward signal), let agents curate suites from traces: 80% traces repeat, but customer shifts trigger changes—agents detect, alert owners, update tests. Run online, always-on optimization; integrate telemetry (errors, costs) for self-correction—heal issues without prediction.",[22,3804,3805],{},"Applies broadly: auto-optimization like Python reward loops tunes anything (e.g., BBQ mixes). Evals become code\u002Fliving agents, not datasets: 80% static intent-defined, 20% adaptive for weird queries. At Comet, they're implementing; mindset: treat evals agentically as problem\u002Fdata shift.",{"title":47,"searchDepth":48,"depth":48,"links":3807},[3808,3809,3810],{"id":3778,"depth":48,"text":3779},{"id":3788,"depth":48,"text":3789},{"id":3798,"depth":48,"text":3799},[],{"content_references":3813,"triage":3821},[3814,3816,3819],{"type":69,"title":3815,"context":72},"OpenClaw",{"type":3817,"title":3818,"context":72},"dataset","ARC-AGI 2",{"type":3744,"title":3820,"context":72},"Adaptive testing for LLM evals paper",{"relevance":77,"novelty":78,"quality":78,"actionability":78,"composite":79,"reasoning":3822},"Category: AI & LLMs. The article discusses the need for adaptive evaluation methods for AI agents, addressing a specific pain point about traditional static benchmarks failing to measure dynamic AI behavior. It provides actionable insights on building self-optimizing evaluation suites that can adapt to user intent, which is directly applicable to product builders working with AI.","\u002Fsummaries\u002Fb24309283167b83a-malleable-evals-adaptive-testing-for-changing-ai-a-summary","2026-05-12 16:00:06","2026-05-13 12:00:22",{"title":3768,"description":47},{"loc":3823},"b24309283167b83a","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=4VhbYlfC7Gs","summaries\u002Fb24309283167b83a-malleable-evals-adaptive-testing-for-changing-ai-a-summary",[93,94,95,96],"https:\u002F\u002Fi.ytimg.com\u002Fvi\u002F4VhbYlfC7Gs\u002Fhqdefault.jpg","Static benchmarks fail self-adapting agents; use production traces for agent-curated, always-on eval suites that self-optimize toward user intent.","[Vincent Koc](https:\u002F\u002Fx.com\u002Fvincent_koc)'s conference talk on why static benchmarks fail for adaptive AI agents like OpenClaw, pushing a shift to \"malleable evals\" where agents self-generate test suites from production traces to handle behavioral drift and edge cases.",[96],"1xd66DttG0MMlsYN5aUG3JKuQffY2t3Ws867t1cXQiI",{"id":3838,"title":3839,"ai":3840,"body":3845,"categories":4365,"created_at":55,"date_modified":55,"description":47,"extension":56,"faq":55,"featured":57,"kicker_label":55,"meta":4366,"navigation":81,"path":4377,"published_at":4378,"question":55,"scraped_at":4379,"seo":4380,"sitemap":4381,"source_id":4382,"source_name":3756,"source_type":3757,"source_url":4383,"stem":4384,"tags":4385,"thumbnail_url":4386,"tldr":4387,"tweet":4388,"unknown_tags":4389,"__hash__":4390},"summaries\u002Fsummaries\u002F05800069e15ecf07-agentic-search-powers-80-of-llm-context-engineerin-summary.md","Agentic Search Powers 80% of LLM Context Engineering",{"provider":7,"model":8,"input_tokens":3841,"output_tokens":3842,"processing_time_ms":3843,"cost_usd":3844},8105,2819,34902,0.00273965,{"type":14,"value":3846,"toc":4357},[3847,3851,3863,3878,3886,3890,3893,3916,3925,3928,3990,3993,3996,4000,4003,4032,4035,4038,4041,4045,4052,4069,4280,4283,4286,4290,4308,4314,4317,4321,4353],[17,3848,3850],{"id":3849},"context-engineering-demystified-agentic-search-at-the-core","Context Engineering Demystified: Agentic Search at the Core",[22,3852,3853,3854,3858,3859,3862],{},"Context engineering selects what enters an LLM's context window from diverse sources like local files, databases, web, working memory, agent skills, and long-term memory. Leonie argues it's 80% agentic search—the mechanisms deciding ",[3855,3856,3857],"em",{},"what"," and ",[3855,3860,3861],{},"how"," to retrieve—over model choice. Early RAG used fixed vector search on user queries, retrieving irrelevant chunks or missing multi-hop needs. Agentic RAG introduces tools letting agents decide: retrieve? Rewrite query? Multi-round? This evolves retrieval from rigid pipelines to dynamic decisions.",[22,3864,3865,3866,3870,3871,3870,3874,3877],{},"Key principle: No single tool suffices. Native tools handle sources (e.g., file search for codebases, SQL\u002FESQL for DBs, web scrapers), but shell tools (LangChain's shell, Anthropic's bash, OpenAI's exec) add versatility via CLI commands like ",[3867,3868,3869],"code",{},"ls",", ",[3867,3872,3873],{},"grep",[3867,3875,3876],{},"curl",". Combine them: Vector for semantics, keyword for exact matches, general-purpose for complex filters. Trade-off: Shell is flexible but risky (security, errors); specialized tools are reliable but narrow.",[22,3879,3880,3881,3885],{},"\"Context engineering is about 80% agentic search because it's this little box right here ",[3882,3883,3884],"span",{},"arrow from sources to window",".\"",[17,3887,3889],{"id":3888},"building-reliable-search-tools-descriptions-and-parameters","Building Reliable Search Tools: Descriptions and Parameters",[22,3891,3892],{},"Effective tools start with precise descriptions. Poor ones: One-sentence generics (\"Search the database\"). Good ones specify:",[3894,3895,3896,3904,3910],"ul",{},[3897,3898,3899,3903],"li",{},[3900,3901,3902],"strong",{},"Core purpose",": What it does.",[3897,3905,3906,3909],{},[3900,3907,3908],{},"Triggers",": When to use (e.g., \"For conference sessions on AI constraints\"), avoid (e.g., \"Not for web data\").",[3897,3911,3912,3915],{},[3900,3913,3914],{},"Relationships",": Sequence (e.g., \"Load ESQL skill first\").",[22,3917,3918,3919,3921,3922,3885],{},"Reinforce in system prompts: \"You are a search agent... decide if retrieval needed. Use ",[3882,3920,69],{}," for ",[3882,3923,3924],{},"condition",[22,3926,3927],{},"Parameter complexity scales failure risk:",[3929,3930,3931,3947],"table",{},[3932,3933,3934],"thead",{},[3935,3936,3937,3941,3944],"tr",{},[3938,3939,3940],"th",{},"Complexity",[3938,3942,3943],{},"Example",[3938,3945,3946],{},"Agent Challenge",[3948,3949,3950,3964,3977],"tbody",{},[3935,3951,3952,3956,3961],{},[3953,3954,3955],"td",{},"Low",[3953,3957,3958],{},[3867,3959,3960],{},"get_customer(id: str)",[3953,3962,3963],{},"Easy ID generation.",[3935,3965,3966,3969,3974],{},[3953,3967,3968],{},"Medium",[3953,3970,3971],{},[3867,3972,3973],{},"semantic_search(query: str, k: int=3, filters: dict)",[3953,3975,3976],{},"Balancing params.",[3935,3978,3979,3982,3987],{},[3953,3980,3981],{},"High",[3953,3983,3984],{},[3867,3985,3986],{},"execute_esql(query: str)",[3953,3988,3989],{},"Full query syntax.",[22,3991,3992],{},"Always add try-except for self-correction: Return errors to agent (e.g., invalid wildcard) instead of crashing. Test tools standalone before agent integration.",[22,3994,3995],{},"\"Tool description is the most important aspect... add trigger conditions, relationships.\"",[17,3997,3999],{"id":3998},"diagnosing-and-fixing-agent-failure-modes","Diagnosing and Fixing Agent Failure Modes",[22,4001,4002],{},"Agents fail in predictable ways—address systematically:",[4004,4005,4006,4012,4018],"ol",{},[3897,4007,4008,4011],{},[3900,4009,4010],{},"No tool called",": Relies on parametric knowledge. Fix: Prompt \"Always retrieve for factual queries.\"",[3897,4013,4014,4017],{},[3900,4015,4016],{},"Wrong tool",": Picks web over DB. Fix: Detailed descriptions + system prompt prioritization.",[3897,4019,4020,4023,4024,4027,4028,4031],{},[3900,4021,4022],{},"Wrong parameters",": E.g., SQL ",[3867,4025,4026],{},"%"," wildcard vs. ",[3867,4029,4030],{},"*"," in ESQL. Fix: Skills for docs.",[22,4033,4034],{},"Quality criteria: Tool returns relevant, non-zero results (zero may signal rewrite). Evaluate: Does output cite retrieved context? Multi-turn coherence?",[22,4036,4037],{},"Common mistake: Over-relying on semantics—fails keywords (\"GPA\" matches \"Gemma\" via tokens). Solution: Hybrid stacks.",[22,4039,4040],{},"\"The most challenging aspect... was getting the agent to not call the web search tool but the database search tool.\"",[17,4042,4044],{"id":4043},"step-by-step-semantic-to-general-purpose-retrieval-with-skills","Step-by-Step: Semantic to General-Purpose Retrieval with Skills",[22,4046,4047,4048,4051],{},"Assumes: Python\u002FLangChain basics, local ElasticSearch cluster, chunked\u002Findexed data (e.g., conference sessions: ",[3867,4049,4050],{},"text"," embedded, metadata filterable).",[22,4053,4054,4057,4058,3870,4061,4064,4065,4068],{},[3900,4055,4056],{},"Prerequisites",": Mid-level (built basic agents); install ",[3867,4059,4060],{},"langchain",[3867,4062,4063],{},"elasticsearch",", embedding model (e.g., ",[3867,4066,4067],{},"gte-large-en-v1.5",").",[4004,4070,4071,4148,4216],{},[3897,4072,4073,4076,4077,4137,4140,4141,4143,4144,4147],{},[3900,4074,4075],{},"Vanilla Semantic Tool"," (Brittle baseline):",[4078,4079,4083],"pre",{"className":4080,"code":4081,"language":4082,"meta":47,"style":47},"language-python shiki shiki-themes github-light github-dark","from langchain_community.vectorstores import ElasticVectorSearch\nfrom langchain.tools import tool\nembeddings = HuggingFaceEmbeddings(model_name=\"thenlper\u002Fgte-large\")\nvectorstore = ElasticVectorSearch(elasticsearch_url, index_name, embeddings)\n@tool\ndef semantic_search(query: str) -> str:\n    \"\"\"Search conference sessions semantically.\"\"\"\n    docs = vectorstore.similarity_search(query, k=3)\n    return \"\\n\".join([doc.page_content for doc in docs])\n","python",[3867,4084,4085,4092,4097,4103,4108,4113,4119,4125,4131],{"__ignoreMap":47},[3882,4086,4089],{"class":4087,"line":4088},"line",1,[3882,4090,4091],{},"from langchain_community.vectorstores import ElasticVectorSearch\n",[3882,4093,4094],{"class":4087,"line":48},[3882,4095,4096],{},"from langchain.tools import tool\n",[3882,4098,4100],{"class":4087,"line":4099},3,[3882,4101,4102],{},"embeddings = HuggingFaceEmbeddings(model_name=\"thenlper\u002Fgte-large\")\n",[3882,4104,4105],{"class":4087,"line":78},[3882,4106,4107],{},"vectorstore = ElasticVectorSearch(elasticsearch_url, index_name, embeddings)\n",[3882,4109,4110],{"class":4087,"line":77},[3882,4111,4112],{},"@tool\n",[3882,4114,4116],{"class":4087,"line":4115},6,[3882,4117,4118],{},"def semantic_search(query: str) -> str:\n",[3882,4120,4122],{"class":4087,"line":4121},7,[3882,4123,4124],{},"    \"\"\"Search conference sessions semantically.\"\"\"\n",[3882,4126,4128],{"class":4087,"line":4127},8,[3882,4129,4130],{},"    docs = vectorstore.similarity_search(query, k=3)\n",[3882,4132,4134],{"class":4087,"line":4133},9,[3882,4135,4136],{},"    return \"\\n\".join([doc.page_content for doc in docs])\n",[4138,4139],"br",{},"Works: \"Regulatory constraints\" → relevant talks.\nFails: \"GPA\" → Gemma models (semantic drift).",[4138,4142],{},"Agent: ",[3867,4145,4146],{},"create_react_agent(llm, [semantic_search], system_prompt)",".",[3897,4149,4150,4153,4154,4209,4211,4212,4215],{},[3900,4151,4152],{},"General-Purpose ESQL Tool"," (More flexible, error-prone):\nSwitch to GPT-4o-mini (nano too weak for query gen).",[4078,4155,4157],{"className":4080,"code":4156,"language":4082,"meta":47,"style":47},"from elasticsearch import Elasticsearch\nclient = Elasticsearch(\"http:\u002F\u002Flocalhost:9200\")\n@tool\ndef execute_esql_query(esql_query: str) -> str:\n    \"\"\"Execute ESQL against conference index. Use ESQL skill first.\"\"\"\n    try:\n        response = client.esql.query(esql={\"query\": esql_query})\n        return json.dumps(response)\n    except Exception as e:\n        return f\"Error: {str(e)}\"\n",[3867,4158,4159,4164,4169,4173,4178,4183,4188,4193,4198,4203],{"__ignoreMap":47},[3882,4160,4161],{"class":4087,"line":4088},[3882,4162,4163],{},"from elasticsearch import Elasticsearch\n",[3882,4165,4166],{"class":4087,"line":48},[3882,4167,4168],{},"client = Elasticsearch(\"http:\u002F\u002Flocalhost:9200\")\n",[3882,4170,4171],{"class":4087,"line":4099},[3882,4172,4112],{},[3882,4174,4175],{"class":4087,"line":78},[3882,4176,4177],{},"def execute_esql_query(esql_query: str) -> str:\n",[3882,4179,4180],{"class":4087,"line":77},[3882,4181,4182],{},"    \"\"\"Execute ESQL against conference index. Use ESQL skill first.\"\"\"\n",[3882,4184,4185],{"class":4087,"line":4115},[3882,4186,4187],{},"    try:\n",[3882,4189,4190],{"class":4087,"line":4121},[3882,4191,4192],{},"        response = client.esql.query(esql={\"query\": esql_query})\n",[3882,4194,4195],{"class":4087,"line":4127},[3882,4196,4197],{},"        return json.dumps(response)\n",[3882,4199,4200],{"class":4087,"line":4133},[3882,4201,4202],{},"    except Exception as e:\n",[3882,4204,4206],{"class":4087,"line":4205},10,[3882,4207,4208],{},"        return f\"Error: {str(e)}\"\n",[4138,4210],{},"Agent generates: ",[3867,4213,4214],{},"from conference_sessions where text like '%GPA%'"," → Error (wrong wildcard). Self-corrects next turn.",[3897,4217,4218,4221,4222],{},[3900,4219,4220],{},"Agent Skills for Progressive Disclosure",":",[3894,4223,4224,4266,4273],{},[3897,4225,4226,4227,4230],{},"Markdown file: ",[3867,4228,4229],{},"skills\u002Fesql.md",[4078,4231,4235],{"className":4232,"code":4233,"language":4234,"meta":47,"style":47},"language-markdown shiki shiki-themes github-light github-dark","---\nname: ESQL Skill\ndescription: Generate ESQL queries.\n---\nStructure: from index | where condition | limit k\nStrings: double quotes. Wildcards: * not %.\n","markdown",[3867,4236,4237,4242,4247,4252,4256,4261],{"__ignoreMap":47},[3882,4238,4239],{"class":4087,"line":4088},[3882,4240,4241],{},"---\n",[3882,4243,4244],{"class":4087,"line":48},[3882,4245,4246],{},"name: ESQL Skill\n",[3882,4248,4249],{"class":4087,"line":4099},[3882,4250,4251],{},"description: Generate ESQL queries.\n",[3882,4253,4254],{"class":4087,"line":78},[3882,4255,4241],{},[3882,4257,4258],{"class":4087,"line":77},[3882,4259,4260],{},"Structure: from index | where condition | limit k\n",[3882,4262,4263],{"class":4087,"line":4115},[3882,4264,4265],{},"Strings: double quotes. Wildcards: * not %.\n",[3897,4267,4268,4269,4272],{},"Tools: ",[3867,4270,4271],{},"create_openai_functions_agent"," + skill loader middleware.",[3897,4274,4275,4276,4279],{},"Updated prompt\u002Ftool: \"Load ESQL skill before execute_esql_query.\"\nResult: Agent loads skill → ",[3867,4277,4278],{},"from conference_sessions where text like '*GPA*' | limit 3"," → Exact match (Samuel's talk).",[22,4281,4282],{},"Fits in workflow: Post-indexing, pre-agent loop. Practice: Index your data, break semantic tool, iterate fixes.",[22,4284,4285],{},"\"Doing good search is incredibly difficult... curate your own stack.\"",[17,4287,4289],{"id":4288},"shell-tools-filesystem-and-beyond","Shell Tools: Filesystem and Beyond",[22,4291,4292,4293,3870,4295,3870,4297,4300,4301,4304,4305,4147],{},"Shell unlocks local files: ",[3867,4294,3869],{},[3867,4296,3873],{},[3867,4298,4299],{},"cat",". LangChain ",[3867,4302,4303],{},"@tool"," wraps ",[3867,4306,4307],{},"subprocess",[22,4309,4310,4311,4313],{},"Limitations: No built-in semantics; security (sandbox). Extend: Custom CLIs (e.g., DB CLI, ",[3867,4312,3876],{}," for web).",[22,4315,4316],{},"Teaser: File search beats naive recursion for code agents; combine with skills for CLI docs.",[17,4318,4320],{"id":4319},"key-takeaways","Key Takeaways",[3894,4322,4323,4326,4329,4332,4335,4338,4341,4344,4347,4350],{},[3897,4324,4325],{},"Prioritize agentic search: 80% of context engineering success.",[3897,4327,4328],{},"Craft tool descriptions with purpose, triggers, relationships; reinforce in prompts.",[3897,4330,4331],{},"Add try-except everywhere—let agents self-correct from errors.",[3897,4333,4334],{},"Use skills for complex params (e.g., query langs); progressive disclosure scales docs.",[3897,4336,4337],{},"Hybrid tools: Semantic for concepts, keyword\u002FESQL for exact, shell for files\u002Fweb.",[3897,4339,4340],{},"Test standalone: Break with keywords\u002Ffilters before agent.",[3897,4342,4343],{},"Model matters: Nano for simple, mini+ for query gen.",[3897,4345,4346],{},"Zero results? Rewrite, don't answer.",[3897,4348,4349],{},"Sequence tools: Skills → General query → Shell fallback.",[3897,4351,4352],{},"Build stacks, not silos: Match source to tool.",[4354,4355,4356],"style",{},"html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"title":47,"searchDepth":48,"depth":48,"links":4358},[4359,4360,4361,4362,4363,4364],{"id":3849,"depth":48,"text":3850},{"id":3888,"depth":48,"text":3889},{"id":3998,"depth":48,"text":3999},{"id":4043,"depth":48,"text":4044},{"id":4288,"depth":48,"text":4289},{"id":4319,"depth":48,"text":4320},[109],{"content_references":4367,"triage":4375},[4368,4370,4373],{"type":69,"title":4369,"context":72},"LangChain",{"type":69,"title":4371,"author":4372,"context":72},"ElasticSearch","Elastic",{"type":3744,"title":4374,"author":4372,"context":72},"ESQL",{"relevance":77,"novelty":78,"quality":78,"actionability":78,"composite":79,"reasoning":4376},"Category: AI & LLMs. The article provides a deep dive into context engineering and agentic search, which are crucial for building AI-powered products. It offers practical insights on tool descriptions and retrieval strategies that can be directly applied by developers and product builders.","\u002Fsummaries\u002F05800069e15ecf07-agentic-search-powers-80-of-llm-context-engineerin-summary","2026-05-08 13:00:06","2026-05-10 15:05:55",{"title":3839,"description":47},{"loc":4377},"de920282ce217f10","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ynJyIKwjonM","summaries\u002F05800069e15ecf07-agentic-search-powers-80-of-llm-context-engineerin-summary",[93,94,95,96],"https:\u002F\u002Fi.ytimg.com\u002Fvi\u002FynJyIKwjonM\u002Fhqdefault.jpg","Context engineering relies on agentic search tools to pull relevant data from files, DBs, web, and memory. Master tool descriptions, skills, and shell tools to avoid brittle retrieval—demoed with ElasticSearch and LangChain.","Practical workshop by Elastic's Leonie Monigatti on building reliable agentic search stacks for LLM context engineering—demos semantic search, database queries (e.g., ESQL\u002FSQL), shell tools, and hybrids, plus tool description tips and failure mode fixes with live code.",[96],"petQDU6OhxttglviH_WlFwK-aisF7C8n9iqKN9uDLhw",{"id":4392,"title":4393,"ai":4394,"body":4399,"categories":4436,"created_at":55,"date_modified":55,"description":47,"extension":56,"faq":55,"featured":57,"kicker_label":55,"meta":4437,"navigation":81,"path":4456,"published_at":4457,"question":55,"scraped_at":4458,"seo":4459,"sitemap":4460,"source_id":4461,"source_name":4462,"source_type":89,"source_url":4463,"stem":4464,"tags":4465,"thumbnail_url":55,"tldr":4466,"tweet":55,"unknown_tags":4467,"__hash__":4468},"summaries\u002Fsummaries\u002Fc770cf1fe76f0f1e-3-steps-to-custom-claude-code-agentic-os-summary.md","3 Steps to Custom Claude Code Agentic OS",{"provider":7,"model":8,"input_tokens":4395,"output_tokens":4396,"processing_time_ms":4397,"cost_usd":4398},7992,1784,32847,0.00246775,{"type":14,"value":4400,"toc":4431},[4401,4405,4408,4411,4415,4418,4421,4425,4428],[17,4402,4404],{"id":4403},"codify-workflows-into-repeatable-skills-and-automations","Codify Workflows into Repeatable Skills and Automations",[22,4406,4407],{},"Break daily personal and business activities into domains (e.g., memory, productivity, research, content, community), then subdivide domains into discrete tasks (e.g., YouTube search, deep research across Twitter\u002FGitHub\u002Fweb\u002FYouTube\u002FObsidian, morning reports, competitor tracking). Convert tasks into consistent skills using Claude Code's skill creator—simple ones like YouTube reports replace manual searches; complex ones like deep research consolidate multi-source data with past Obsidian entries.",[22,4409,4410],{},"Turn suitable skills into automations: local for on-device tasks, remote for API-driven ones (Claude Code decides type). Use a single prompt in Claude Code terminal (microphone-enabled stream-of-consciousness recommended) to iterate: describe day-to-day tasks\u002Fdomains, let it propose skills\u002Fautomations per domain. This creates a trackable backbone—execute skills identically every time, eliminating random prompting. Value scales to teams\u002Fclients: hand off system for consistent results without deep Claude expertise.",[17,4412,4414],{"id":4413},"implement-obsidian-memory-layer-for-persistence","Implement Obsidian Memory Layer for Persistence",[22,4416,4417],{},"Designate an Obsidian vault as the OS home (Claude Code runs from here). Use Karpathy-inspired structure: \u002Fraw (dumping\u002Fstaging for chats\u002Fresearch), \u002Fwiki (codified articles from raw, e.g., RAG system reports), \u002Foutputs (final artifacts like slide decks). Customize further: subfolders per domain (research, AI agency, sales) for intuitive data flow.",[22,4419,4420],{},"Create claude.md in vault root—appended to every prompt—to define OS purpose, behaviors, and exact folder structure (e.g., archive, content, ops, personal, projects, raw, wiki). This enables efficient navigation, lower token costs, and adherence to flows. Obsidian's Markdown suffices as lightweight RAG—no vector DB needed for most; Claude Code handles retrieval fine. Track\u002Foptimize outputs here since all skills\u002Fautomations populate it.",[17,4422,4424],{"id":4423},"deploy-observability-dashboard-for-visibility-and-accessibility","Deploy Observability Dashboard for Visibility and Accessibility",[22,4426,4427],{},"Build a web dashboard exposing key skills\u002Fautomations as clickable buttons (e.g., \"Deep Research\" auto-populates prompt, runs headless Claude Code instance via --headless flag, outputs to Obsidian with source links). Use Claude Code prompt to generate: conversation identifies skills for buttons, custom observability metrics (5-hour\u002Fweekly usage, daily routines count, vault changes, forecasts).",[22,4429,4430],{},"Overcomes terminal limits—visualize what terminal can't (e.g., usage trends). Ideal for non-technical teams\u002Fclients: anyone clicks buttons for Claude power without terminal\u002FVS Code. Fully customizable per user\u002Fclient needs. Combine with architecture\u002Fmemory for end-to-end OS: optimize via tracking, scale via sharing.",{"title":47,"searchDepth":48,"depth":48,"links":4432},[4433,4434,4435],{"id":4403,"depth":48,"text":4404},{"id":4413,"depth":48,"text":4414},{"id":4423,"depth":48,"text":4424},[112],{"content_references":4438,"triage":4453},[4439,4443,4446,4448,4451],{"type":3744,"title":4440,"url":4441,"context":4442},"Master Claude Code","https:\u002F\u002Fwww.skool.com\u002Fchase-ai","recommended",{"type":3744,"title":4444,"url":4445,"context":4442},"Chase AI Community","https:\u002F\u002Fwww.skool.com\u002Fchase-ai-community",{"type":69,"title":4447,"context":72},"Obsidian",{"type":3744,"title":4449,"author":4450,"context":64},"Karpathy Obsidian RAG setup","Andrej Karpathy",{"type":69,"title":4452,"context":72},"Claude Code",{"relevance":77,"novelty":78,"quality":78,"actionability":77,"composite":4454,"reasoning":4455},4.55,"Category: AI Automation. The article provides a detailed framework for codifying workflows into automations using Claude Code, which directly addresses the audience's need for practical applications in AI integration. It offers specific steps for implementation, such as creating a structured Obsidian vault and utilizing a dashboard for observability, making it highly actionable.","\u002Fsummaries\u002Fc770cf1fe76f0f1e-3-steps-to-custom-claude-code-agentic-os-summary","2026-05-05 03:37:16","2026-05-05 16:07:17",{"title":4393,"description":47},{"loc":4456},"a91bfd724607582d","Chase AI","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Bgxsx8slDEA","summaries\u002Fc770cf1fe76f0f1e-3-steps-to-custom-claude-code-agentic-os-summary",[93,95,94,96],"Codify workflows into domains, tasks, skills, and automations; add Obsidian memory layer; build observability dashboard to track, optimize, and share with teams\u002Fclients ahead of 99% of users.",[96],"hqDGxnjTNUTthpKqY-trk_chizcVFbgro_HLw835dWQ"]